README.md

Python for AI-Driven Automation & Business Data Science

From your first line of Python to shipping a real AI-driven automation —
a hands-on, self-paced curriculum across Python fluency, business data science,
machine learning, AI engineering, and production.

81 runnable notebooks · 11 modules · 300+ exercises · 100% offline

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Contents

• Why this course
• Quick start
• Choose your path
• Curriculum
• Repository layout
• How each notebook works
• LLM providers
• Open any notebook in Colab — one-click links to all 81 notebooks
• Contributing & licence

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Why this course

• End to end. From print("hello") to a deployed, scheduled AI automation — no gaps assumed, no steps skipped.
• Runs anywhere. One click into Google Colab, or pip install locally. Every notebook runs 100% offline — no API key, no paid service required.
• Learn by doing. 300+ exercises — including a deliberate 🐞 debug-me in each lesson — every one shipping with a worked solution and the reasoning behind it.
• Modern, minimal code. Charts in 1–3 lines (pandas .plot(), seaborn, sklearn's built-in plot helpers), pipelines over boilerplate — you learn the way practitioners actually write Python today.
• Visual where it counts. Key ideas — train/test splits, k-fold cross-validation, grid search, RAG pipelines, MCP topology — come with clean diagrams embedded right in the notebooks.
• Real business problems. Churn & CLV, fraud detection, demand forecasting, customer segmentation, RAG assistants, and AI governance — not toy datasets.

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Quick start

▶︎ Google Colab — zero setup (recommended)

Click a badge to open a notebook in your browser; nothing to install.

🔑 You need a free Google account to run the notebooks. Colab gives each signed-in user a free cloud runtime, so the first time you press Run it will ask you to sign in (any Gmail account works). Without signing in you can read a notebook but not execute its cells. No Google account? Use the Local Jupyter option below instead.

• Start the full course — 00_master_onboarding.ipynb  
• Start the fast track — 00_fast_track_onboarding.ipynb  
• See it work first (5 min) — 00c_see_it_work.ipynb  

Every notebook is listed with its own Colab link in Open any notebook in Colab.

⌥ Local Jupyter

python -m venv .venv
source .venv/bin/activate         # Windows: .venv\Scripts\activate
pip install -r requirements.txt
jupyter lab

Tested with Python 3.10+. Module 0 includes an environment-check cell. The 11 appendices demo heavier libraries (PyTorch, Prophet, FAISS, LangChain, …), kept commented-out at the bottom of requirements.txt — each still runs offline via a built-in stand-in, so install them only to see the real library at work.

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Choose your path

	🎓 Complete course	🏎️ Fast track
Scope	All 11 modules + 11 optional appendices	The essentials, condensed
Notebooks	42 lessons (+ appendices)	14 lessons
Time	~115 hours	~15 hours
Best for	Depth — every exercise, stretch problem & capstone	A credible end-to-end pass in a few evenings
Start here	00_master_onboarding.ipynb	fast_track/

New here? 00c_see_it_work.ipynb is a 5-minute offline demo of what you'll build, and 00b_course_overview.ipynb has the full module map and an interactive time estimator.

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Curriculum

Module	Lessons	Focus
0 · Onboarding	—	Setup, orientation, 5-minute demo
1 · Foundations	1–6	Variables, control flow, lists, dicts, functions, classes
2 · Data Science	7–11	pandas, NumPy, seaborn/matplotlib, statistics, time series
3 · Real-world I/O	12–13	HTTP/APIs, SQL, data validation
4 · Machine Learning	14–16	scikit-learn, cross-validation & hyperparameter tuning, model evaluation, feature engineering
5 · AI Engineering	17–21	Prompts, RAG, agents, document processing, eval & observability
6 · Production	22–23	Packaging notebooks into projects, scheduling
7 · Capstones	24–25	Two end-to-end projects (analytics + AI assistant)
8 · Business AI	26–29	Digital transformation, architecture, AI-assisted dev, governance
9 · Building AI POCs	30–34	LLM theory, Copilot setup, three POCs, RAG deep dive, vector DBs + agents
10 · Industry Applications	35–38	Churn/CLV, fraud, segmentation + recommenders, demand & maintenance
11 · Agents, Tools & MCP	39–42	Agent architectures, robust tools, the Model Context Protocol, multi-agent systems
12 · Optional: Text Analytics	43–45	Topic modeling (BERTopic, STREAM) & sentiment analysis (VADER → classical → transformers)
13 · Optional: DeepTab	46	Deep learning for tabular data — Mamba/FT-Transformer/SAINT… behind a scikit-learn API

11 optional appendices (classical → deep-learning → foundation-model forecasting, PyTorch, vector stores, RAG/agent frameworks) live beside their modules — all runnable offline. Modules 12–13 are optional, reference-style tracks (text analytics + deep tabular), also fully offline.

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Repository layout

Path	Contents
00_onboarding/ … 11_agents_tools_mcp/	The complete course — 42 lessons + 11 appendices
12_optional/, 13_DeepTab/	Optional reference tracks — text analytics (NB 43–45) & deep tabular learning (NB 46)
fast_track/	The fast track — 14 trimmed notebooks (~15 h)
quizzes/	10 short multiple-choice quizzes (Modules 1–6 & 8–11)
data/	Sample CSVs the notebooks read (support_ops, api_log, customer_feedback)
slides/	Course-overview deck + lecture decks (PDF + LaTeX source)
scripts/	Helpers to run every notebook end-to-end or check NB-number references
docs/	Course-design notes (pedagogical review, module-descriptor coverage)
llm_providers.py	Unified interface to OpenAI / Anthropic / Google / Ollama (+ offline MockLLM)
previous_versions/	The legacy flat 19-notebook layout, archived

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How each notebook works

A consistent six-part template:

🎯 Objectives + ✅ prerequisites → numbered concept sections (prose + runnable code) → 🧪 practice exercises (incl. a 🐞 debug-me) → 🧠 stretch exercises A–D → 🎁 bonus mini-project → ✅ self-assessment + 🚀 next step

Every exercise — 300+ across the course — ships with a worked solution and the reasoning behind it.

Two conventions you'll see throughout:

• Charts take a few lines, not a page. Plots are drawn with pandas .plot(), seaborn one-liners (hue= instead of loops, sns.heatmap(cm, annot=True) for matrices), and scikit-learn's *Display helpers — raw matplotlib appears only for final tweaks and multi-panel layouts.
• Diagrams travel with the notebook. Explanatory figures (the train/test split, 5-fold cross-validation, the GridSearchCV flow, RAG pipelines, MCP host/client/server topology, walk-forward backtesting, …) are embedded as attachments inside the .ipynb files, so they render on GitHub, in Colab, and locally with no extra image files.

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LLM providers

Notebooks 17–21 and 25 run entirely offline with the built-in MockLLM. For real intelligence, swap one line — the unified interface in llm_providers.py supports four providers:

Provider	Class	When to use
🟢 OpenAI	OpenAILLM(model="gpt-4o-mini")	Reliable default
🟠 Anthropic	AnthropicLLM(model="claude-haiku-4-5-20251001")	Long context, careful tone
🔵 Google	GoogleLLM(model="gemini-2.0-flash")	Cheap at scale
🟣 Ollama	OllamaLLM(model="llama3.2:3b")	Local — no internet, key, or cost

Set the matching *_API_KEY env var for hosted providers (never inline). See 05_ai_engineering/A1_llm_providers_guide.ipynb for setup and cost notes. Never commit API keys.

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🚀 Open any notebook in Colab

Every notebook below runs in Google Colab with one click — no install, no download. Click a badge to open it. Sign in with a free Google account the first time you run a cell — Colab needs it to give you a cloud runtime.

0 · Onboarding

Notebook	Open
00_master_onboarding.ipynb
00b_course_overview.ipynb
00c_see_it_work.ipynb

1 · Foundations

Notebook	Open
01_python_basics.ipynb
02_control_structures.ipynb
03_lists_data_structures.ipynb
04_dictionaries_advanced.ipynb
05_functions_modules.ipynb
06_classes_and_oop.ipynb

2 · Data Science

Notebook	Open
07_pandas_fundamentals.ipynb
08_numpy_fundamentals.ipynb
09_matplotlib_basics.ipynb
10_statistics_basics.ipynb
11_time_series_forecasting.ipynb
A1_forecasting_classical.ipynb
A2_forecasting_prophet_libraries.ipynb
A3_forecasting_deep_learning.ipynb
A4_forecasting_foundation_models.ipynb

3 · Real-world I/O

Notebook	Open
12_apis_and_http.ipynb
13_sql_fundamentals.ipynb
A1_web_scraping_firecrawl.ipynb

4 · Machine Learning

Notebook	Open
14_sklearn_basics.ipynb
15_model_evaluation.ipynb
16_feature_engineering.ipynb
A1_pytorch_foundations.ipynb
A2_pytorch_vision_and_sequences.ipynb
A3_pytorch_fine_tuning.ipynb
A4_tabpfn_priorlab.ipynb

5 · AI Engineering

Notebook	Open
17_ai_workflows.ipynb
18_embeddings_retrieval.ipynb
19_tools_and_agents.ipynb
20_document_processing.ipynb
21_ai_evaluation_observability.ipynb
A1_llm_providers_guide.ipynb
A2_vector_stores_survey.ipynb
A3_rag_and_agent_frameworks.ipynb

6 · Production

Notebook	Open
22_from_notebook_to_project.ipynb
23_scheduling_orchestration.ipynb

7 · Capstones

Notebook	Open
24_capstone_analytics.ipynb
25_capstone_ai_assistant.ipynb

8 · Business AI

Notebook	Open
26_digital_transformation.ipynb
27_architecture_patterns.ipynb
28_ai_assisted_software_development.ipynb
29_bpm_governance_poc_mvp.ipynb

9 · Building AI POCs

Notebook	Open
30_llm_fundamentals.ipynb
31_from_setup_to_first_poc.ipynb
32_three_pocs_growing_complexity.ipynb
33_rag_pipeline_deep_dive.ipynb
34_vector_db_and_agentic_ai.ipynb

10 · Industry Applications

Notebook	Open
35_churn_clv_retention.ipynb
36_fraud_anomaly_detection.ipynb
37_segmentation_recommenders.ipynb
38_demand_maintenance.ipynb

11 · Agents, Tools & MCP

Notebook	Open
39_agent_architectures.ipynb
40_designing_robust_tools.ipynb
41_model_context_protocol.ipynb
42_multi_agent_systems.ipynb

12 · Optional: Text Analytics

Notebook	Open
43_topic_modeling_bertopic.ipynb
44_topic_modeling_stream.ipynb
45_sentiment_analysis.ipynb

13 · Optional: DeepTab

Notebook	Open
46_deeptab_tabular_deep_learning.ipynb

🏎️ Fast track

Notebook	Open
00_fast_track_onboarding.ipynb
01_python_basics.ipynb
02_control_structures.ipynb
03_lists_and_dicts.ipynb
04_functions.ipynb
05_classes_basics.ipynb
06_pandas_fundamentals.ipynb
07_visualization_and_stats.ipynb
08_sklearn_basics.ipynb
09_apis_and_sql.ipynb
10_ai_workflows.ipynb
11_embeddings_and_rag.ipynb
12_tools_and_agents.ipynb
13_notebook_to_project.ipynb
14_agents_and_mcp.ipynb

🧠 Quizzes

Notebook	Open
quiz_01_foundations.ipynb
quiz_02_data_science.ipynb
quiz_03_real_world_io.ipynb
quiz_04_machine_learning.ipynb
quiz_05_ai_engineering.ipynb
quiz_06_production.ipynb
quiz_08_business_ai.ipynb
quiz_09_building_ai_pocs.ipynb
quiz_10_industry_applications.ipynb
quiz_11_agents_tools_mcp.ipynb

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Contributing & licence

Spotted a bug or an unclear explanation? Open an issue or PR — contributions are welcome.

Licensed under the MIT License (see LICENSE) — use freely for learning, teaching, or anything else.

_{Happy coding 🚀}